| | |
| | | { |
| | | __shared__ float part[BLOCK]; |
| | | int i,b; |
| | | int filter = (blockIdx.x + blockIdx.y*gridDim.x); |
| | | int filter = blockIdx.x; |
| | | int p = threadIdx.x; |
| | | float sum = 0; |
| | | for(b = 0; b < batch; ++b){ |
| | |
| | | { |
| | | int size = convolutional_out_height(layer)*convolutional_out_width(layer); |
| | | |
| | | |
| | | learn_bias<<<cuda_gridsize(layer.n), BLOCK>>>(layer.batch, layer.n, size, layer.delta_gpu, layer.bias_updates_gpu); |
| | | learn_bias<<<layer.n, BLOCK>>>(layer.batch, layer.n, size, layer.delta_gpu, layer.bias_updates_gpu); |
| | | check_error(cudaPeekAtLastError()); |
| | | } |
| | | |
| | |
| | | gemm_ongpu(0,0,m,n,k,1.,a,k,b,n,1.,c+i*m*n,n); |
| | | } |
| | | activate_array_ongpu(layer.output_gpu, m*n*layer.batch, layer.activation); |
| | | cuda_pull_array(layer.output_gpu, layer.output, m*n*layer.batch); |
| | | //for(i = 0; i < m*n*layer.batch; ++i) printf("%f, ", layer.output[i]); |
| | | //printf("\n"); |
| | | } |
| | | |
| | | extern "C" void backward_convolutional_layer_gpu(convolutional_layer layer, float *in, float *delta_gpu) |
| | |
| | | extern "C" void update_convolutional_layer_gpu(convolutional_layer layer) |
| | | { |
| | | int size = layer.size*layer.size*layer.c*layer.n; |
| | | |
| | | /* |
| | | cuda_pull_array(layer.bias_updates_gpu, layer.bias_updates, layer.n); |
| | | cuda_pull_array(layer.biases_gpu, layer.biases, layer.n); |
| | | cuda_pull_array(layer.filter_updates_gpu, layer.filter_updates, size); |
| | | cuda_pull_array(layer.filters_gpu, layer.filters, size); |
| | | printf("Bias: %f updates: %f\n", mse_array(layer.biases, layer.n), mse_array(layer.bias_updates, layer.n)); |
| | | printf("Filter: %f updates: %f\n", mse_array(layer.filters, layer.n), mse_array(layer.filter_updates, layer.n)); |
| | | */ |
| | | |
| | | axpy_ongpu(layer.n, layer.learning_rate, layer.bias_updates_gpu, 1, layer.biases_gpu, 1); |
| | | scal_ongpu(layer.n,layer.momentum, layer.bias_updates_gpu, 1); |
| | | |